---
title: 'Llamaindex Examples Example'
description: 'LlamaIndex AgentOps Integration Example'
---
{/*  SOURCE_FILE: examples/llamaindex_examples/llamaindex_example.ipynb  */}

_View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/examples/llamaindex_examples/llamaindex_example.ipynb'} target={'_blank'}>Github</a>_

# LlamaIndex AgentOps Integration Example

This notebook demonstrates how to use AgentOps with LlamaIndex for observability and monitoring of your context-augmented generative AI applications.

## Setup

First, install the required packages:


```
# Install required packages
!pip install agentops llama-index-instrumentation-agentops llama-index-embeddings-huggingface llama-index-llms-huggingface python-dotenv
```

## Initialize AgentOps Handler

Set up the AgentOps handler for LlamaIndex instrumentation:


```
import os
from dotenv import load_dotenv
from llama_index.core import VectorStoreIndex, Document, Settings
from llama_index.instrumentation.agentops import AgentOpsHandler

# Initialize AgentOps handler
handler = AgentOpsHandler()
handler.init()

# Load environment variables
load_dotenv()

# Set API keys (replace with your actual keys)
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_agentops_api_key_here")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
```

## Configure Local Models (Optional)

For this example, we'll use local HuggingFace models to avoid requiring external API keys:


```
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM

# Configure local embeddings and LLM
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.llm = HuggingFaceLLM(model_name="microsoft/DialoGPT-medium")
print("Using local HuggingFace embeddings and LLM")
```

## Create Sample Documents and Index

Create some sample documents and build a vector index:


```
print("🚀 Starting LlamaIndex AgentOps Integration Example")
print("=" * 50)

# Create sample documents
documents = [
    Document(text="LlamaIndex is a framework for building context-augmented generative AI applications with LLMs."),
    Document(text="AgentOps provides observability into your AI applications, tracking LLM calls, performance metrics, and more."),
    Document(text="The integration between LlamaIndex and AgentOps allows you to monitor your RAG applications seamlessly."),
    Document(text="Vector databases are used to store and retrieve embeddings for similarity search in RAG applications."),
    Document(text="Context-augmented generation combines retrieval and generation to provide more accurate and relevant responses.")
]

print("📚 Creating vector index from sample documents...")
index = VectorStoreIndex.from_documents(documents)
print("✅ Vector index created successfully")
```

## Perform Queries

Now let's perform some queries to demonstrate the AgentOps integration:


```
# Create query engine
query_engine = index.as_query_engine()

print("🔍 Performing queries...")

# Sample queries
queries = [
    "What is LlamaIndex?",
    "How does AgentOps help with AI applications?",
    "What are the benefits of using vector databases in RAG?"
]

for i, query in enumerate(queries, 1):
    print(f"\n📝 Query {i}: {query}")
    response = query_engine.query(query)
    print(f"💬 Response: {response}")
```

## Results

After running this notebook, you should see:

1. **AgentOps Session Link**: A URL to view the session in your AgentOps dashboard
2. **Cost Tracking**: Information about the cost of LLM calls (if using paid APIs)
3. **Operation Tracking**: All LlamaIndex operations are automatically tracked

Check your AgentOps dashboard to see detailed information about:
- LLM calls and responses
- Performance metrics
- Cost analysis
- Session replay

The session link will be printed in the output above by AgentOps.


```
print("\n" + "=" * 50)
print("🎉 Example completed successfully!")
print("📊 Check your AgentOps dashboard to see the recorded session with LLM calls and operations.")
print("🔗 The session link should be printed above by AgentOps.")
```


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